{"id":95,"date":"2019-07-23T12:06:05","date_gmt":"2019-07-23T10:06:05","guid":{"rendered":"https:\/\/project.inria.fr\/aaltd19\/?page_id=95"},"modified":"2020-06-17T19:30:32","modified_gmt":"2020-06-17T17:30:32","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/aaltd19\/accepted-papers\/","title":{"rendered":"Accepted papers"},"content":{"rendered":"<p>The PDF article are available since authors give their agreement to make them appear here. If not available, please contact directly the authors for any request.<\/p>\n<p>Oral presentations:<\/p>\n<ul>\n<li>Harjit Hullait, David Leslie, Nicos Pavlidis and Steve King &#8211; Robust Functional Regression for Outlier Detection, <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Hullait.pdf\">AALTD_19_Hullait<\/a><\/li>\n<li>Ma\u00ebl Guillem\u00e9, Simon Malinowski, Romain Tavenard and Xavier Renard &#8211; Localized Random Shapelet <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Guilleme.pdf\">AALTD_19_Guilleme<\/a><\/li>\n<li>Matthew Middlehurst, William Vickers and Anthony Bagnall &#8211; Scalable Dictionary Classifiers for Time Series Classification <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Middlehurst.pdf\">AALTD_19_Middlehurst<\/a><\/li>\n<li>Kriti Kumar, Angshul Majumdar, Girish Chandra and A Anil Kumar &#8211; Transform Learning Based Function Approximation for Regression and Forecasting <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Kumar.pdf\">AALTD_19_Kumar<\/a><\/li>\n<li>Vincent Lemaire, Fabien Boitier, Jelena Pesic, Alexis Bondu, St\u00e9phane Ragot and Fabrice Cl\u00e9rot &#8211; Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Lemaire.pdf\">AALTD_19_Lemaire<\/a><\/li>\n<li>Matthieu Boussard and Tom Puech &#8211; A fully automated periodicity detection in time series <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Boussard.pdf\">AALTD_19_Boussard<\/a><\/li>\n<li>Bart van der Lugt and Ad Feelders &#8211; Conditional Forecasting of Water Level Time Series with RNNs <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_vanDerLugt.pdf\">AALTD_19_vanDerLugt<\/a><\/li>\n<li>Jingwei Zuo, Karine Zeitouni and Yehia Taher &#8211; Incremental and Adaptive Feature Exploration over Time Series Stream <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Zuo.pdf\">AALTD_19_Zuo<\/a><\/li>\n<li>George Oastler and Jason Lines &#8211; A Significantly Faster Elastic Ensemble for Time Series Classification <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Oaslter.pdf\">AALTD_19_Oaslter<\/a><\/li>\n<li>Marieke Vinkenoog, Mart Janssen and Matthijs van Leeuwen &#8211; Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Vinkenoog.pdf\">AALTD_19_Vinkenoog<\/a><\/li>\n<li>Jo\u00e3o Mendes-Moreira and Mitra Baratchi &#8211; Reconciling predictions in the regression setting: an application to bus travel time prediction <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_MendesMoreira.pdf\">AALTD_19_MendesMoreira<\/a><\/li>\n<\/ul>\n<p>Posters:<\/p>\n<ul>\n<li>Christopher Martin Amadeus Bonenberger, Benjamin Kathan and Wolfgang Ertel &#8211; Feature-Based Gait Pattern Classification for a Robotic Walking Frame <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Bonenberger.pdf\">AALTD_19_Bonenberger<\/a><\/li>\n<li>Dihia Boulegane, Albert Bifet and Giyyarpuram Madhusudan &#8211; Arbitrated Dynamic Ensemble with Abstaining for Time Series Forecasting on Data Streams <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Boutegane.pdf\">AALTD_19_Boutegane<\/a><\/li>\n<li>Harish S. Bhat and Shagun Rawat &#8211; Learning Stochastic Dynamical Systems via Bridge Sampling<\/li>\n<li>Daniel Shen and Min Chi &#8211; An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Bhat.pdf\">AALTD_19_Bhat<\/a><\/li>\n<li>Lucas Foulon, Serge Fenet, Christophe Rigotti and Denis Jouvin &#8211; Detecting Anomalies over Message Streams in Railway Communication Systems<\/li>\n<li>Cl\u00e9ment Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard and Manel Boumghar &#8211; How to detect novelty in textual data streams? A comparative study of existing methods.<\/li>\n<li>Katarzyna Juraszek, Nidhi Saini, Marcela Charfuelan, Holmer Hemsen and Volker Markl &#8211; Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Juraszek.pdf\">AALTD_19_Juraszek<\/a><\/li>\n<li>Edouard Pineau, S\u00e9bastien Razakarivony and Thomas Bonald &#8211; Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Pineau.pdf\">AALTD_19_Pineau<\/a><\/li>\n<li>Yildiz Karadayi &#8211; Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Karadayi.pdf\">AALTD_19_Karadayi<\/a><\/li>\n<li>Mohammad Al-Naser, Takehiro Niikura, Sheraz Ahmed, Hiroki Ohashi, Takuto Sato, Mitsuhiro Okada, Katsuyuki Nakamura and Andreas Dengel &#8211; Quantifying Quality of Actions Using Wearable Sensor <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_AlNaser.pdf\">AALTD_19_AlNaser<\/a><\/li>\n<li>Kezi Yu, Yunlong Wang and Yong Cai &#8211; Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets <a href=\"https:\/\/project.inria.fr\/aaltd19\/files\/2019\/08\/AALTD_19_Yu.pdf\">AALTD_19_Yu<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The PDF article are available since authors give their agreement to make them appear here. If not available, please contact directly the authors for any request. Oral presentations: Harjit Hullait, David Leslie, Nicos Pavlidis and Steve King &#8211; Robust Functional Regression for Outlier Detection, AALTD_19_Hullait Ma\u00ebl Guillem\u00e9, Simon Malinowski, Romain\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/aaltd19\/accepted-papers\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":958,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-95","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/pages\/95","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/users\/958"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/comments?post=95"}],"version-history":[{"count":5,"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/pages\/95\/revisions"}],"predecessor-version":[{"id":139,"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/pages\/95\/revisions\/139"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/aaltd19\/wp-json\/wp\/v2\/media?parent=95"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}